TeraPRI: Homomorphic Terahertz-Empowered Joint Wireless Power and Information Transfer With Privacy-Preserving for 6G-Autonomous Vehicles

In sixth-generation networks, Terahertz technology enables high-speed, low-latency communication capabilities, while Mobile Edge Computing (MEC) enhances remote computation, leveraging Autonomous Vehicles (AVs) capable of providing computational resources and energy support, particularly in challenging environments where conventional terrestrial infrastructure is absent. However, challenges remain, particularly with the limited energy of end-user nodes and the constrained data capabilities of MEC nodes in THz networks, which require substantial energy to transmit large volumes of data. Efficient coordination of energy supply and information transfer is essential. Additionally, such networks can face signal interference and leakage of privacy-sensitive information, complicating data security and reliable communication. To address these challenges, we propose TeraPRI, a novel framework for homomorphic THz-empowered joint wireless power and information transfer with privacy preservation for 6G Autonomous Vehicles. First, we introduce a dual-mode adaptive resource allocation method that alternates between “harvest-and-then-transmit-mode” (HaT-mode) and “transmit-and-then-harvest-mode” (TaH-mode) based on end-user node demands. Second, we design a selective CKKS homomorphic encryption technique with lightweight thresholding for frequency allocation, enabling the MEC to securely assign unique THz sub-channels to end-user nodes in the low-THz band (0.1–1.0 THz) while enhancing communication security through randomized frequency hopping. Extensive simulations show that TeraPRI significantly improves power transfer, information transfer rates, and privacy over existing methods.